Dynamic faceted search on a document corpus

ABSTRACT

A query-focused faceted structure generation method, system, and computer program product for generating a query-focused faceted structure from a taxonomy for searching a document corpus, including augmenting taxonomy types with new instances where the instances comprise entities within a proximity of existing instances of taxonomy types in a local embedding of entities parsed from the document corpus, ranking each instance in the augmented taxonomy with respect to its type as a function of both a distance from an instance to a query in a global embedding vector space of the entities trained from the document corpus and a distance of an instance to a type in the local embedding, and ranking the taxonomy types using expanded instances in the document corpus for each type.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a related application of co-pending U.S.patent application Ser. No. ______, IBM Docket No. P201809462US01, whichis filed concurrently herewith, the entire contents of which areincorporated herein by reference.

BACKGROUND

The present invention relates generally to a query-focused facetedstructure generation method, and more particularly, but not by way oflimitation, to a system, method, and computer program product forgenerating a query-focused faceted structure from a taxonomy forsearching a document collection.

With an enormous amount of unstructured data available in documents, itis important for customers to rapidly narrow down the search over alarge corpus in a structured manner and find relevant informationquickly.

The growing amount of data that customers experience requires moreefficient and accessible ways to organize and find specializedinformation using their own terminology.

Faceted search involves augmenting a document retrieval system with afaceted navigation system to allow users to narrow down search resultsby filtering based on a faceted structure.

Conventional facet generating approaches in this space have drawbackssuch as that the documents must be tagged with an existing hierarchywhich needs to be consistent, adding overhead in content curation andmanagement and these static facets are not based on the matchingdocuments or query. Using other conventional approaches, large numbersof facets are generated which can be overwhelming to the users and makeit difficult to navigate and facets based on standard approaches such ascorpus-term frequency may produce facets that are not related to theimportant information in the query. Also, there is no provision formanagement of the facets generated without adding/editing the originaldocuments, etc.

SUMMARY

Based on the above, the inventors have identified a need in the art thata better method of generating facets for search is needed.

In an exemplary embodiment, the present invention provides acomputer-implemented query-focused faceted structure generation methodfor generating a query-focused faceted structure from a taxonomy forsearching a document collection, the method including ingesting adocument corpus, generating a vector space representation of a query andinstances from a taxonomy of the document corpus, and producing adynamic structure of a relevant category and facet using a two-vectorspace representation from the generated vector space representation.

One or more other exemplary embodiments include a computer programproduct and a system, based on the method described above.

Other details and embodiments of the invention will be described below,so that the present contribution to the art can be better appreciated.Nonetheless, the invention is not limited in its application to suchdetails, phraseology, terminology, illustrations and/or arrangements setforth in the description or shown in the drawings. Rather, the inventionis capable of embodiments in addition to those described and of beingpracticed and carried out in various ways and should not be regarded aslimiting.

As such, those skilled in the art will appreciate that the conceptionupon which this disclosure is based may readily be utilized as a basisfor the designing of other structures, methods and systems for carryingout the several purposes of the present invention. It is important,therefore, that the claims be regarded as including such equivalentconstructions insofar as they do not depart from the spirit and scope ofthe present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the invention will be better understood from the followingdetailed description of the exemplary embodiments of the invention withreference to the drawings, in which:

FIG. 1 exemplarily shows a high-level flow chart for a query-focusedfaceted structure generation method 100 according to an embodiment ofthe present invention;

FIG. 2 exemplarily depicts definitions of terms according to anembodiment of the present invention;

FIGS. 3-4 exemplarily depict a flow chart of the invention according toan embodiment of the present invention;

FIG. 5 exemplarily depicts examples of input taxonomy of types andinstances according to an embodiment of the present invention;

FIGS. 6-7 exemplarily depict examples of facets and an output accordingto an embodiment of the present invention;

FIG. 8 exemplarily depicts an input and an output according to anembodiment of the present invention;

FIG. 9 exemplarily depicts dynamic faceted search trains using two-wordembedding models according to an embodiment of the present invention;

FIG. 10 depicts a cloud-computing node 10 according to an embodiment ofthe present invention;

FIG. 11 depicts a cloud-computing environment 50 according to anembodiment of the present invention; and

FIG. 12 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The invention will now be described with reference to FIGS. 1-12, inwhich like reference numerals refer to like parts throughout. It isemphasized that, according to common practice, the various features ofthe drawings are not necessarily to scale. On the contrary, thedimensions of the various features can be arbitrarily expanded orreduced for clarity.

By way of introduction of the example depicted in FIG. 1, an embodimentof a query-focused faceted structure generation method 100 according tothe present invention can include various steps for enhancing robustnessof a neural network.

By way of introduction of the example depicted in FIG. 10, one or morecomputers of a computer system 12 according to an embodiment of thepresent invention can include a memory 28 having instructions stored ina storage system to perform the steps of FIG. 1.

Although one or more embodiments may be implemented in a cloudenvironment 50 (e.g., FIG. 12), it is nonetheless understood that thepresent invention can be implemented outside of the cloud environment.

Terms used in the descriptions of the steps 101-104 below areexemplarily defined in FIG. 2.

With reference to FIGS. 1 and 3-4, in step 101, a document corpus isingested. That is, in step 101, a corpus of unstructured/plain textdocuments is an input and several steps are performed on the corpus. Thesteps include, a step of pre-processing by splitting the corpus intosentences. Moreover, in this step, chunks are annotated using a shallowsyntactic parser. For a second step, candidate hypernym-hyponym pairsare extracted by using unsupervised hypernym induction directly fromtext such as in co-pending Application IBM Reference No. P201807822. Inanother step, a taxonomy is built by grouping hyponyms by hypernyms andform hierarchies. In this step a higher accuracy is obtained for domainspecific large corpus by splitting the corpus into N dataset. Theintuition for this is that domain specific taxonomy should be consistentacross different dataset of same domain. These steps are repeated toobtain N set of hypernym-hyponym pairs. Thus, any pair that appears inless than M set (where M<N) are removed and the remaining hyponyms aregrouped by hypernyms and form hierarchies.

To enable navigation to related types and entities not in the documentcorpus, hyponyms are matched against entity types, concepts, or classesin a knowledge graph. Hypernyms can be matched against entities,instances, sub-concepts, or sub-classers in the same knowledge graphusing Jaro-winkler distance (approximate string matching, editdistance). WordNet I used as the knowledge graph but others such asWikidata may also be used. Synonyms for hypernyms are generated frommatching entities etc. in the knowledge graph. The intuition behind thisis that the knowledge graph supports structured navigation to relatedinformation (e.g., Wikipedia® pages for people, places, and things) notcontained in the corpus.

It is noted that the Jaro-winkler distance is a string metric measuringan edit distance between two sequences. Other metrics can be used tomeasure distance between the strings.

In another embodiment, in step 101, noun words and phrases (terminology)are filtered from the shallow parsed chunks. While noun phrases (NPchunks) are extracted, other part of speech patterns are also extracted.The invention uses about 100 patterns that include adjectives, numbers,and so on. Part of speech patterns from titles of encyclopedia articlesare run through a part-of-speech tagger and the most frequent patternsare retained, The patterns are then matched against the chunks toextract a sequence of words. A ‘type model’ is trained that generates aphrase embedding of the terminology in the document corpus (Word2VecCBOW, narrow context window). Further, a ‘topic model’ is trained thatgenerates a second phrase embedding of the terminology in the documentcorpus (Word2Vec skip-gram, large context window). Both Word2Vec modelsare two-layer neural networks trained to reconstrute the linguisticcontext of the phrases. Word2Vec takes input plain text from thedocument corpus and produces a vector space of a large number ofdimensions (e.g, 100) with each unique phrase in the corpus beingassigned a corresponding vector in the soace, Word vectors arepositioning in the vector space such that phrases that share contexts inthe fcorpus are located proximally in the vector space. The window isthe size of the context used. CBOW is a particular method for usingWord2Vec that uses a continuous bag of words (or in our case, phrases)where order does not matter. SkipGram is another method of usingWord2Vec that uses n-grams but with the possibility of skipped words.The taxonomy is exemplarily shown in FIG. 5. And, the type model andtopic model are exemplarily shown in FIG. 9.

In step 102, a vector space representation of a query and instances froma taxonomy of the document corpus is generated. To restrict taxonomyinduction to domain specific terminology the invention filters any termin the induced taxonomy that is not part of the domain specificterminology extracted by techniques such as in co-pending U.S. PatentApplication No. US20180276196A1 incorporated by reference. If the corpusis heterogeneous (i.e., contains documents of multiple sub-domains),terminologies of the underlying sub-domains (in the corpus) areextracted using techniques such as in co-ending U.S. patent applicationSer. No. 15/881,521 incorporated by reference.

The three steps above are followed to extract hypernym-hyponym pairs.During these steps, the invention constructs taxonomies for each of thesub-domain by making sure that the terms in a taxonomy for a sub-domainare part of the corresponding extracted terminology of that sub-domain.

Also, in step 102, the vector space representation is performed by wordembedding. To perform the word embedding, different embedding models aretrained to predict semantic and syntactic similarity of keywords. Forexample, a type model may be used such as word2vec with CBOW method,narrow context window for modeling functional and syntactic similarity.And, a topic model may be used by using word2vec with skip-gram method,wide context window for modeling topical similarity.

In this technique, nearest neighbors of the embeddings were evaluated tobe useful for modeling similar and related keywords. User studies wereperformed on a full Wikipedia™ dataset to evaluate the quality ofsimilar keywords and related keywords as returned by the models. Both ofthe models achieved high precision. And, analysis was performed on an ITsupport dataset to evaluate if the embeddings can support a typical userquery in the IT support use case. The models were able to generatemeaningful similar and related keywords that guide a user's explorationin different directions.

In another embodiment, in step 102, a taxonomy is loaded that includes agraph of type and instance nodes where instances have a consistentrelationship to type. A database structure, XML or OWL file, or othertechnical means may be used to encode a graph of type and instancenodes.

In step 103, a dynamic structure of relevant facet categories and facetvalues is produced using two vector space representations (e.g., asshown in FIGS. 6-7). It is noted that the two models are not combinedinto one, but they are both used. For facet category generation, theinput is a query vector and the output is a list of facet categories. Alist of instances in the taxonomy with a cosine similarity in the Topicmodel closest to the query vector are retrieved as facet values. Thenthe types in the taxonomy are ordered by the number of matchinginstances with the highest number first. One issue is that the facetcategory may not have enough facet values as required by the user.Therefore, for each type, more instances are obtained from the documentcorpus that are similar to the centroid of instances from taxonomyinduction in the Type vector space. These are filtered using terminologyfrom search results (e.g., relevant docs) so that only facet valuesappearing in the search results are included. Then, the top n facetvalues are presented for each facet category. The top n facets aredetermined by sorting by a combination (multiplication) of the cosinedistance to the centroid of the instances vectors in the Type vectorspace and the cosine distance to the query vector in the Topic vectorspace1 Using both scores is designed to make sure that the facet valuesare relevant to both the facet category and the query.

In step 103, the vector space representation of a user query andinstances from a taxonomy is generated, a vector for the user query(e.g., a weighted combination of the vectors for each query token in thetopic model) is generated as query vector, and a list of vectors isgenerated for instances from the taxonomy in the topic model.

That is, in step 103, a dynamic structure of facet categories and facetvalues is produced using the two vector space representations, the Typevector space and the Topic vector space. To do so, a K (parameter) isselected with nearest neighbor instances to the query vector fromtaxonomy instances using the Topic Model as query-similar instances, anM (parameter) is selected for types in the taxonomy with the most numberof query-similar instances to use as categories, and an N (parameter)facet values is selected from instances of the types corresponding toeach of the M facet categories. The N facet values are expanded withineach of the M facet categories to get more category-similar instancesfrom the document corpus using the Type model. Thus, some facets may notbe in either the query or the taxonomy (discoverable). The facet valuesare ranked within each of the M categories by distance to both the queryvector (e.g., in topic model vector space) and to the centroid of the Ninstances that correspond to the category (e.g., in type model vectorspace).

In step 104, the dynamic structure is returned as a data file to a user.The facet values are filtered from the above by the terms from thesearch result documents, and the facets are kept that appear in thesearch document results.

FIG. 8 exemplarily depicts an input and the dynamic structure (output)returned as the data file to the user.

That is, steps 101-104 include steps for generating a query-focusedfaceted structure from a taxonomy for searching a document collection byaugmenting taxonomy types with new instances where the instances areentities within a proximity of existing instances of taxonomy types in alocal embedding of entities parsed from the document collection, rankingeach instance in the augmented taxonomy with respect to its type as afunction of both the distance from the instance to the query in a globalembedding vector space of the entities trained from the documentcollection and the distance of the instance to the type in the localembedding, ranking the taxonomy types using their expanded instances inthe document collection for each type, and presenting a facetedstructure for narrowing a search of the document collection to a user,the faceted structure generated by selecting the ranked taxonomy typesas search categories and ranked instances within each type as searchfacets within each category.

In one embodiment, the taxonomy may be a knowledge graph, a glossary, ahierarchical taxonomy, a DAG taxonomy, an ontology, a database schema, atype system. In another embodiment, the type model is a Word2vec cbowmodel, with a context window below a threshold, autoencoders and thetopic model is a Word2Vec with a context window above a threshold, LSA,PCA.

As noted above, the taxonomy was generated from an interactiveprocessing using the corpus (e.g., spreadsheet patent in references), anautomatic process using the corpus (e.g., hypernym induction inreferences) where the automatic process merges taxonomies generated fromdifferent sub-collection of the document collection.

In another embodiment, the distance metric may be a cosine-similarity, aPearson, a Minkowski, a Euclidian, etc. And, in one embodiment, thefaceted structures includes elements (types, entities) not generatedfrom the document collection where the types in the knowledge graph areretrieved using lexical synonyms from multi-word types and where theinstances in the knowledge graph are retrieved using approximate stringmatching.

Exemplary Aspects, Using a Cloud Computing Environment

Although this detailed description includes an exemplary embodiment ofthe present invention in a cloud computing environment, it is to beunderstood that implementation of the teachings recited herein are notlimited to such a cloud computing environment. Rather, embodiments ofthe present invention are capable of being implemented in conjunctionwith any other type of computing environment now known or laterdeveloped.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client circuits through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 20, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablenode and is not intended to suggest any limitation as to the scope ofuse or functionality of embodiments of the invention described herein.Regardless, cloud computing node 10 is capable of being implementedand/or performing any of the functionality set forth herein.

Although cloud computing node 10 is depicted as a computer system/server12, it is understood to be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with computersystem/server 12 include, but are not limited to, personal computersystems, server computer systems, thin clients, thick clients, hand-heldor laptop circuits, multiprocessor systems, microprocessor-basedsystems, set top boxes, programmable consumer electronics, network PCs,minicomputer systems, mainframe computer systems, and distributed cloudcomputing environments that include any of the above systems orcircuits, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingcircuits that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage circuits.

Referring now to FIG. 10, a computer system/server 12 is shown in theform of a general-purpose computing circuit. The components of computersystem/server 12 may include, but are not limited to, one or moreprocessors or processing units 16, a system memory 28, and a bus 18 thatcouples various system components including system memory 28 toprocessor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further described below, memory 28 mayinclude a computer program product storing one or program modules 42comprising computer readable instructions configured to carry out one ormore features of the present invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may be adapted for implementation in anetworking environment. In some embodiments, program modules 42 areadapted to generally carry out one or more functions and/ormethodologies of the present invention.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing circuit, other peripherals,such as display 24, etc., and one or more components that facilitateinteraction with computer system/server 12. Such communication can occurvia Input/Output (I/O) interface 22, and/or any circuits (e.g., networkcard, modem, etc.) that enable computer system/server 12 to communicatewith one or more other computing circuits. For example, computersystem/server 12 can communicate with one or more networks such as alocal area network (LAN), a general wide area network (WAN), and/or apublic network (e.g., the Internet) via network adapter 20. As depicted,network adapter 20 communicates with the other components of computersystem/server 12 via bus 18. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system/server 12. Examples, include, but arenot limited to: microcode, circuit drivers, redundant processing units,external disk drive arrays, RAID systems, tape drives, and data archivalstorage systems, etc.

Referring now to FIG. 11, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing circuits used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingcircuit. It is understood that the types of computing circuits 54A-Nshown in FIG. 11 are intended to be illustrative only and that computingnodes 10 and cloud computing environment 50 can communicate with anytype of computerized circuit over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 12, an exemplary set of functional abstractionlayers provided by cloud computing environment 50 (FIG. 11) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 12 are intended to be illustrative only andembodiments of the invention are not limited thereto. As depicted, thefollowing layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage circuits 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and query-focused faceted structuregeneration method 100 in accordance with the present invention.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Further, Applicant's intent is to encompass the equivalents of all claimelements, and no amendment to any claim of the present applicationshould be construed as a disclaimer of any interest in or right to anequivalent of any element or feature of the amended claim.

What is claimed is:
 1. A computer-implemented query-focused facetedstructure generation method for generating a query-focused facetedstructure from a taxonomy for searching a document corpus, the methodcomprising: augmenting taxonomy types with new instances where theinstances comprise entities within a proximity of existing instances oftaxonomy types in a local embedding of entities parsed from the documentcorpus; ranking each instance in the augmented taxonomy with respect toits type as a function of both a distance from an instance to a query ina global embedding vector space of the entities trained from thedocument corpus and a distance of an instance to a type in the localembedding; and ranking the taxonomy types using expanded instances inthe document corpus for each type.
 2. The method of claim 1, presentinga dynamic structure including a faceted structure for a narrowing searchof the document corpus to a user, the faceted structure being generatedby selecting the ranked taxonomy types as search categories and rankedinstances within each type as search facets within each category.
 3. Themethod of claim 1, further comprising returning the dynamic structure asa data file to a user.
 4. The method of claim 2, further comprisingreturning the dynamic structure as a data file to a user.
 5. The methodof claim 1, further comprising ingesting the document corpus by:extracting the terminology that includes noun words and phrases from thedocument corpus to: train a type model that generates a phrase embeddingof the terminology in the document corpus; and train a topic model thatgenerates a second phrase embedding of the terminology in the documentcorpus.
 6. The method of claim 1, wherein the taxonomy types are loadedand includes a graph of type and instance nodes where instances have aconsistent relationship to type.
 7. The method of claim 1, embodied in acloud-computing environment.
 8. A computer program product forquery-focused faceted structure generation, the computer program productcomprising a computer-readable storage medium having programinstructions embodied therewith for generating a query-focused facetedstructure from a taxonomy for searching a document corpus, the programinstructions executable by a computer to cause the computer to perform:augmenting taxonomy types with new instances where the instancescomprise entities within a proximity of existing instances of taxonomytypes in a local embedding of entities parsed from the document corpus;ranking each instance in the augmented taxonomy with respect to its typeas a function of both a distance from an instance to a query in a globalembedding vector space of the entities trained from the document corpusand a distance of an instance to a type in the local embedding; andranking the taxonomy types using expanded instances in the documentcorpus for each type.
 9. The computer program product of claim 8,presenting a dynamic structure including a faceted structure for anarrowing search of the document corpus to a user, the faceted structurebeing generated by selecting the ranked taxonomy types as searchcategories and ranked instances within each type as search facets withineach category.
 10. The computer program product of claim 8, furthercomprising returning the dynamic structure as a data file to a user. 11.The computer program product of claim 9, further comprising returningthe dynamic structure as a data file to a user.
 12. The computer programproduct of claim 8, further comprising ingesting the document corpus by:extracting the terminology that includes noun words and phrases from thedocument corpus to: train a type model that generates a phrase embeddingof the terminology in the document corpus; and train a topic model thatgenerates a second phrase embedding of the terminology in the documentcorpus.
 13. The computer program product of claim 8, wherein thetaxonomy types are loaded and includes a graph of type and instancenodes where instances have a consistent relationship to type.
 14. Aquery-focused faceted structure generation system for generating aquery-focused faceted structure from a taxonomy for searching a documentcorpus, the system comprising: a processor; and a memory, the memorystoring instructions to cause the processor to perform: augmentingtaxonomy types with new instances where the instances comprise entitieswithin a proximity of existing instances of taxonomy types in a localembedding of entities parsed from the document corpus; ranking eachinstance in the augmented taxonomy with respect to its type as afunction of both a distance from an instance to a query in a globalembedding vector space of the entities trained from the document corpusand a distance of an instance to a type in the local embedding; andranking the taxonomy types using expanded instances in the documentcorpus for each type.
 15. The system of claim 14, presenting a dynamicstructure including a faceted structure for a narrowing search of thedocument corpus to a user, the faceted structure being generated byselecting the ranked taxonomy types as search categories and rankedinstances within each type as search facets within each category. 16.The system of claim 14, further comprising returning the dynamicstructure as a data file to a user.
 17. The system of claim 15, furthercomprising returning the dynamic structure as a data file to a user. 18.The system of claim 14, further comprising ingesting the document corpusby: extracting the terminology that includes noun words and phrases fromthe document corpus to: train a type model that generates a phraseembedding of the terminology in the document corpus; and train a topicmodel that generates a second phrase embedding of the terminology in thedocument corpus.
 19. The system of claim 14, wherein the taxonomy typesare loaded and includes a graph of type and instance nodes whereinstances have a consistent relationship to type.
 20. The system ofclaim 14, embodied in a cloud-computing environment.